class_docstr: Transformer estimator
  
  Wraps an preprocessing pipeline in a cross-validation strategy.
  
  Parameters
  __________
  preprocessing: obj
      preprocessing pipeline to construct transformer from.
  
  indexer: str, obj
      a cross-validation strategy. Either a :mod`mlens.index` indexer
      instance or a string.
  
  name: str
      name of transformer.
  
  verbose: bool, int (default = False)
      whether to report completed fits.
  
  backend: str (default='threading')
      parallel backend. One of ``'treading'``, ``'multiprocessing'`` and
      ``'sequential'``.
  
  n_jobs: int (default=-1)
      degree of concurrency. Set to ``-1`` for maximum and ``1`` for
      sequential processing.
code: def __init__(
      self,
      preprocessing,
      indexer,
      verbose=False,
      backend=None,
      n_jobs=-1,
      dtype=None,
  ):
      super(TransformerEstimator, self).__init__()
  
      self.preprocessing = preprocessing
      self.indexer = indexer
      self.verbose = verbose
      self.backend = backend
      self.n_jobs = n_jobs
      self.dtype = dtype
  
      self.__static__.extend(["preprocessing", "indexer"])
  
imports: from ..parallel import Layer, make_group
  from ..parallel.base import ParamMixin
  from ..parallel.wrapper import EstimatorMixin
  from ..parallel._base_functions import check_stack
  from ..externals.sklearn.base import BaseEstimator
  from ..externals.sklearn.base import clone
label_desc: Use image node_id 1 to create a new TransformerEstimator object from inherited base classes: BaseEstimator with example: obj = TransformerEstimator(preprocessing, indexer, verbose, backend, n_jobs, dtype)